Biostatistik in Ophthalmologie und Optometrie: Eine Artikelserie zur Unterstützung evidenzbasierter Entscheidungsprozesse Teil 1: Grundlagen statistischer Methoden in der Augenheilkunde und Optometrie

Purpose. Advancements in eye and vision care hinge on the rigorous application of research and the precise interpretation of clinical data. However, the field of Eye and Vision Research (EVR)frequently encounters research waste attributed to methodological flaws and improper statistical analyses, undermining the validity of studies and inefficiently utilising substantial financial resources. This paper, the first instalment in the series “Advancing Statistical Literacy in Eye Care: A Series for Enhanced Clinical Decision-Making,” aims to address these challenges by enhancing the statistical literacy of eye care professionals. Material and Methods. Through a comprehensive narrative literature review and the generation of simulated clinical datasets, this study identifies essential statistical concepts, common pitfalls, and best practices pertinent to EVR. The literature review used multiple databases, including PubMed, Scopus, and Web of Science, focusing on peer-reviewed articles and professional textbooks relevant to statistical methodologies. Simulated datasets reflecting realistic clinical measurements, such as pupil diameter, refractive error, central corneal thickness, and intraocular pressure, were created using Python (V3.12.4) to illustrate key statistical principles and their applications. Results. The paper explores fundamental statistical concepts, including data types (nominal, ordinal, metric), data preparation techniques, handling missing data and outliers, and applying descriptive statistics. Additionally, it explores data distribution characteristics, normality assessment, and data transformation methods to ensure robust and reliable statistical analyses. By bridging theoretical knowledge with practical examples, this instalment seeks to equip eye care professionals with the tools to critically evaluate research, integrate evidence-based practices, and contribute meaningfully to the scientific community. Conclusion. This study establishes a foundational framework to enhance statistical literacy among eye care professionals by exploring essential statistical concepts and best practices in EVR. By addressing common methodological flaws and improper analyses, it aims to reduce research waste and improve the validity of studies. Ultimately, this initiative is expected to promote more accurate data interpretation, better clinical decision-making, and improved patient care in the field of eye and vision health.